“The Microsoft Fabric implementation program gave our data engineering team a structured path from legacy pipelines to a modern lakehouse architecture.”
Enterprise Program Brief
PySpark for Data Engineering
Use PySpark to build reliable notebook transformations. Proposed instructor-led duration; delivery subject to environment and lab validation.
VNode ITeSBridge
Download Program Brief (PDF)Program Snapshot
Engagement scoped to cohort
Duration24 hours
FormatPrivate cohort
LevelBridge
Course CodeB01
- MCT-led delivery
- Private cohort / virtual ILT
- Typical response: 1 business day
Scoping call typically scheduled within one business day
VNode ITeS delivers PySpark for Data Engineering as an MCT-led VNode ITeS program for SQL-oriented data engineers. Typical duration is 24 hours (Private cohort). Delivery stays scoped to how your teams will actually use the platform after the room empties. Private cohorts can shift emphasis by role mix, workspace or repo constraints, and rollout timing.
Audience Profile
Built for these roles
SQL-oriented data engineers
- Tailored Team Delivery
Overview
Executive overview
Use PySpark to build reliable notebook transformations.
Environment and licensing
Fabric capacity and isolated workspace Author and consumer personas Source files and notebook runtime Data-agent labs: paid F2+ or qualifying P1+ capacity, tenant AI settings and source access as documented; cohort capacity sized separately Delivery depends on environment and lab validation. This outline does not include step-by-step lab instructions.
Prerequisites
- SQL and data engineering fundamentals
- SQL and Data Engineering Foundations, or equivalent demonstrable experience
Program Outcomes
Capabilities your teams will gain
- Deliver a parameterised PySpark transformation notebook
- Match reference SQL and verify duplicates, late records and replay
- Quarantine invalid data and protect configured connections
- Explain execution, quality and performance decisions
Enterprise Delivery
Private cohort delivery
VNode-designed, vendor-aligned instructor-led program. Scope role mix, delivery format, and rollout goals for your cohort.
Curriculum
Curriculum roadmap
Proposed instructor-led duration; delivery subject to environment and lab validation.
- Python-to-Spark bridge
- Transformations and joins
- Incremental writes and quality
- Notebook engineering assessment
1Module 1 · 6 hours
Python-to-Spark bridge
+
Module 1 · 6 hours
Python-to-Spark bridge
Read and inspect typed source data in a notebook.
Python syntax essentials
- Use Python expressions and functions needed for notebooks
- Separate driver code from distributed operations
Spark execution
- Explain Spark sessions and lazy execution
- Inspect execution plans at a basic level
DataFrames
- Create and inspect DataFrames
- Choose built-in functions rather than unnecessary row loops
Explicit schemas
- Declare schemas and validate types
- Handle malformed and missing fields explicitly
Concept and design 3h · applied exercise 2.25h · review 0.75h. Included within module duration; no extra hours. Refine during delivery planning without silently changing published course hours.
Applied exercise: Python-to-Spark bridge
Read and inspect typed source data in a notebook.
Expected output. Typed ingestion notebook
Acceptance. The notebook reads typed data and explains where transformations execute.
2Module 2 · 6 hours
Transformations and joins
+
Module 2 · 6 hours
Transformations and joins
Translate analytical SQL transformations into PySpark.
Expressions
- Use column expressions for filtering and derivation
- Handle nulls and dates deliberately
Joins
- Choose join types and validate cardinality
- Identify skew and broadcast concepts without premature tuning
Aggregations
- Aggregate at a declared business grain
- Compare results with reference SQL
Window functions
- Use windows for ranking and latest-record selection
- Apply deterministic deduplication rules
Concept and design 3h · applied exercise 2.25h · review 0.75h. Included within module duration; no extra hours. Refine during delivery planning without silently changing published course hours.
Applied exercise: Transformations and joins
Translate analytical SQL transformations into PySpark.
Expected output. Equivalent PySpark transformation set
Acceptance. PySpark outputs reconcile to reference SQL and preserve the intended grain.
3Module 3 · 6 hours
Incremental writes and quality
+
Module 3 · 6 hours
Incremental writes and quality
Implement an incremental write and test duplicate and malformed inputs.
Delta writes
- Choose append, overwrite or merge semantics
- Explain transactional table updates
Merge concepts
- Design an incremental merge around stable keys
- Handle late and corrected records
Partition choices
- Choose partitions according to access patterns
- Avoid unnecessary small-file proliferation conceptually
Quality and reruns
- Implement completeness and uniqueness checks
- Verify repeated execution does not duplicate outputs
Concept and design 3h · applied exercise 2.25h · review 0.75h. Included within module duration; no extra hours. Refine during delivery planning without silently changing published course hours.
Applied exercise: Incremental writes and quality
Implement an incremental write and test duplicate and malformed inputs.
Expected output. Incremental write and validation evidence
Acceptance. Incremental updates and replay produce expected results with visible quality exceptions.
4Module 4 · 6 hours
Notebook engineering assessment
+
Module 4 · 6 hours
Notebook engineering assessment
Deliver a parameterised notebook with reconciliation and failure reporting.
Parameters
- Expose source and processing-date parameters
- Avoid environment-specific hard-coded values
Functions
- Separate reusable transformation functions
- Keep validation logic testable
Error handling
- Raise actionable errors and report failed checks
- Retain reconciliation evidence
Performance basics
- Inspect unnecessary shuffles and driver collection
- Explain performance trade-offs using observed behaviour
Concept and design 1.5h · applied exercise 3h · review 1.5h. Included within module duration; no extra hours. Refine during delivery planning without silently changing published course hours.
Capstone: Notebook engineering assessment
Deliver a parameterised notebook with reconciliation and failure reporting.
Expected output. Assessed transformation notebook
Acceptance. The parameterised notebook passes reference, failure and rerun cases.
Capstone
Deliver a parameterised notebook with reconciliation and failure reporting. Assessed transformation notebook Evidence of relevant access, quality and risk controls Demonstration and limitations record
Assessment
Scenario-based practical assessment Deliver a parameterised PySpark transformation notebook 30% Match reference SQL and verify duplicates, late records and replay 25% Quarantine invalid data and protect configured connections 25% Explain execution, quality and performance decisions 20% Proposed VNode outline-level assessment; no vendor accreditation or completed lab validation implied.
Participant deliverables
- Assessed transformation notebook
- Evidence of relevant access, quality and risk controls
- Demonstration and limitations record
Scope boundaries
Step-by-step lab instructions; Runnable solution code; Packaged source datasets; Executed environment validation
Delivery options
Count teaching hours, not calendar days. Exclude breaks. Split into 4-hour or 8-hour teaching sessions; schedule complex project work in contiguous blocks.
Delivery Models
Delivery models
Virtual ILTOnsiteHybridExecutive WorkshopBootcampWeekend
Engagement Fit
Engagement fit
- Private cohort delivery
- Bridge practitioner depth
- Business outcome alignment
Enterprise Customization
Enterprise customization
Tailor this program to your organization's priorities: Use PySpark to build reliable notebook transformations.
- Private cohort scheduling
- Equivalent-experience entry review
Resources
Program resources
Yes. Most enterprise clients prefer private delivery scoped to role mix, timezone, and rollout timeline. We align lab environments and scenarios to your tenant context where applicable.
Enterprise Proof
Trusted delivery outcomes
“We needed a partner who understood both the technical depth of Azure OpenAI and the governance requirements of an enterprise.”
Banking & Finance
Representative Enterprise Banking Team
The focus was not just on tooling knowledge, but on helping teams work from a shared operating model as they adopted a more modern data platform.
- Clearer platform operating model across teams
- Improved confidence in modern data stack adoption
Healthcare
Representative Healthcare Product Team
The engagement helped product and engineering stakeholders move from interest in AI to clearer implementation choices, security expectations, and prototyping discipline.
- Stronger alignment between product and engineering teams
- Improved clarity on prototype-to-production requirements
Delivery Capability
Enterprise-grade instruction
MCT-led delivery
Programs led by Microsoft Certified Trainer practitioners
Enterprise program oversight
Founder-led specialist delivery with structured rollout planning
Global delivery
APAC · EMEA · Americas · Virtual & Onsite
Implementation-focused
Hands-on labs aligned to production scenarios
